Data & AnalyticsTechnologyPlatinum

Analyze user retention and behavior over time.

Cohort Analysis Designer

SQL, BigQuery, Snowflake, Data Visualization

advancedv5.0

Best for

▸Designing time-based cohort analyses to measure user retention across monthly sign-up cohorts
▸Building behavioral cohorts comparing feature adopters vs non-adopters for product impact analysis
▸Creating SQL implementations for rolling vs bounded retention calculations in BigQuery/Snowflake
▸Analyzing revenue cohort performance for SaaS subscription businesses using NRR/GRR metrics

What you'll get

▸SQL query template with CTEs for time-based cohort creation, retention calculation logic, and cohort matrix pivoting
▸Cohort analysis framework document specifying cohort definitions, retention metrics, statistical significance tests, and visualization specifications
▸Retention curve interpretation guide explaining normal retention patterns, statistical significance, and actionable insights derivation

Expects

Clear retention measurement objective, available data schema with user IDs and timestamps, and definition of what constitutes an 'active' user event.

Returns

Complete cohort analysis framework including SQL implementation, retention matrix structure, visualization recommendations, and statistical interpretation guidance.

What's inside

“You are a Cohort Analysis Designer. You translate product questions into cohort retention metrics, build statistically rigorous analyses, and extract actionable insights. - **Design before querying**: Define cohort dimension, retention metric, and active event before writing SQL. Vague analyses wast...”

Covers

What You Do DifferentlyMethodologyWatch For

Not designed for ↓

×Basic SQL query writing without cohort-specific time-series logic
×General customer segmentation without time-based retention measurement
×One-off retention calculations without systematic cohort framework design
×Statistical modeling beyond descriptive cohort analysis (survival analysis, predictive churn)

SupaScore

86.55▼

Research Quality (15%)

8.5

Prompt Engineering (25%)

9.1

Practical Utility (15%)

8.5

Completeness (10%)

8.25

User Satisfaction (20%)

8.65

Decision Usefulness (15%)

8.5

Evidence Policy

Standard: no explicit evidence policy.

cohort-analysisretentionproduct-analyticssql-analyticsbehavioral-segmentationretention-curveuser-retentionchurn-analysisdata-visualizationacquisition-cohortssurvival-analysisheatmap

Research Foundation: 8 sources (3 official docs, 1 industry frameworks, 2 academic, 2 books)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.